heavy-tailed noise

Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise COLT 2023 Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tailed Noise NeurIPS 2023 Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be ICLR 2023 Sparse PCA Beyond Covariance Thresholding COLT 2023 Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent JMLR 2022 Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise NeurIPS 2022 Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise ICLR 2022 Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections ICML 2021 Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs NeurIPS 2021 Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance NeurIPS 2021 Distributed High-dimensional Regression Under a Quantile Loss Function JMLR 2020 Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks NeurIPS 2020 Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks NeurIPS 2020 Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations NeurIPS 2020 Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping NeurIPS 2020 Why are Adaptive Methods Good for Attention Models? NeurIPS 2020 Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression COLT 2019 Bayesian Optimization under Heavy-tailed Payoffs NeurIPS 2019 Learning with Non-Convex Truncated Losses by SGD UAI 2019 Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization ICML 2019 Regularized Modal Regression with Applications in Cognitive Impairment Prediction NeurIPS 2017 No-Regret Algorithms for Heavy-Tailed Linear Bandits ICML 2016